Our hybrid approach creates benefits from quantum and classical hardware available today. Our proprietary algorithm suite, at the heart of what we do, offers applications in three key categories: optimization, simulation and machine learning.
Realize the benefits of quantum computing – before the competition – through our pioneering software applications and unique approach.
We take on any multi-variable optimization problems – from discrete, to continuous, to black box. Our unique approach, Quantum Encoding (QuEnc), uses exponentially fewer quantum resources and handles complex data combinations.
We enhance prediction accuracy and learning capacity through our hybrid quantum machine learning techniques. This means better quality answers with less data.
Our tensor-network-based approach enables large-scale simulation of dynamical systems exponentially faster and with greater accuracy.
Our hybrid algorithms are hardware agnostic. This approach makes any investment scalable for future applications, ensuring long-term sustainability.
The workflow scheduling problem is a known NP-hard class of scheduling problems and a major issue in the automotive industry. The quality control testing of manufactured cars carried out by factory workers at the end of an assembly line are constrained by several factors, including the number of workers available over time and some tests being dependent on others being completed first.
In collaboration with the Volkswagen Group Data:Lab Munich, Terra Quantum has produced a paper that investigates this industrial use case. The joint research team compared problem instances with a number of quantum, classical, and hybrid quantum-classical algorithms.
The team developed a novel QUBO to represent the scheduling problem, illustrating how the QUBO complexity depends on the input problem. They present a decomposition method for this specific application to mitigate this complexity, showing how effective the approach is.
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